Safety Advancing Federated Estimation of Risk using AI (SAFER AI)

数据隐私和安全是采用人工智能/机器学习与物联网（物联网）的关键问题，特别是在数据是个人或安全敏感的情况下。支持物联网的设备和机器的数量呈指数级增长，预计到2030年全球将达到260亿。创建优化的平台，在可信任的分布式系统的边缘最大化私有和安全的机器学习，是一个紧迫的优先事项。该联盟的使命是帮助解决在物联网中实施值得信赖的人工智能的挑战。这将通过加速开发针对物联网和边缘系统中的机器学习进行优化的联合、安全、隐私保护和可审计的物联网人工智能平台来实现。OctaiPipe是首个将保护隐私的机器学习技术、网络安全、持续协作学习和人工智能生命周期管理相结合的创新产品。这将使支持物联网的企业能够构建、部署和管理机器学习软件，以保证设备数据及其使用的隐私和安全性，从而使用户对嵌入其中的人工智能解决方案具有高度信任。许多组织已经通过各种工业物联网设备和摄像头收集高级运营和HSE事件数据智能，以成功预测安全事件。然而，基于此的分析对于驱动预防性降低风险的变更并不是有意义的可操作的。对于有意义的可操作的预测，它们必须在工作组内足够的粒度级别上进行。现在存在的技术可以在粒度上监视这些事件——使模型的构建能够在足够的粒度上预测和预测未来的事件——在工作组级别上实现改变游戏规则的预防性影响。幸运的是，HSE关键事件在单个站点甚至组织中都很少见，这意味着没有足够的数据来使用ML模型来预测何时以及为什么会发生HSE事件，从而可以预防这些事件。然而，人工智能解决方案的进展受到以下因素的阻碍：a)需要比一个组织单独产生的更多的观察结果，因此必须共享数据；b)到目前为止尚未克服的跨组织共享数据的障碍。联邦学习解决了这个问题。该项目将使组织能够将数据结合起来，为小型工作组提供可操作的事件预测。该项目旨在解决物联网机器学习中的漏洞，特别关注FL，并解决对社会负责的人工智能应用于社会的基本挑战。

Data privacy and security is a key concern for the adoption of Artificial Intelligence/Machine Learning with IoT (Internet of Things), particularly where that data is personal or security sensitive. The number of IoT-enabled devices and machines is growing exponentially, estimated to reach 26Bn globally by 2030\. Creating platforms optimised to maximise private and secure machine learning at the edge in distributed systems that can be trusted is an urgent priority.The consortium's mission is to help solve the challenge of implementing trustworthy AI-in-IoT. This will be achieved by accelerating the development of a federated, secure, privacy-preserving, and auditable AI-for-IoT platform optimised for machine learning in IoT and edge systems. OctaiPipe is a first-of-its-kind innovation that combines privacy-preserving machine learning technology, cyber security, continuous collaborative learning and AI lifecycle management. This will allow IoT-enabled businesses to build, deploy, and manage machine learning software that guarantees the privacy and security of device data and its use, allowing the user to have a high degree of trust in the AI solutions embedded in them.Many organisations already collect high-level operational and HSE incident data intelligence through various Industrial IoT devices and cameras to successfully predict safety incidents. However, analytics based on this is not meaningfully actionable to drive changes that preventatively reduce risks.For predictions to be meaningfully actionable, they must be made at a sufficient level of granularity within the workgroup. Technology now exists to monitor such events at granularity---enabling the build of models to predict and forecast events in the future at sufficient granularity---enabling game-changing preventative impact at the workgroup level.Fortunately, HSE critical events are rare within single sites or even organisations---meaning insufficient data exists to employ ML models capable of predicting when and why H&S incidents might occur so they can be prevented. However, progress towards an AI-enabled solution is impeded by:a) a requirement for more observations than one organisation can generate alone, so it is imperative to share data, andb) barriers to sharing data across organisations that, until now, have not been overcome.Federated Learning solves this. The project will enable organisations to combine data to facilitate actionable incident predictions for small work groups.This project aims to address vulnerabilities in Machine Learning for IoT with a specific focus on FL and addresses the fundamental challenges of socially responsible AI adoption into society.